# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import division
from __future__ import print_function

import paddle
import paddle.nn as nn
import numpy as np

from paddle.utils.download import get_weights_path_from_url

__all__ = ['resnet18', 'resnet50', 'resnet101']

model_urls = {
	'resnet18': ('https://paddle-hapi.bj.bcebos.com/models/resnet18.pdparams',
	             'cf548f46534aa3560945be4b95cd11c4'),
	'resnet50': ('https://paddle-hapi.bj.bcebos.com/models/resnet50.pdparams',
	             'ca6f485ee1ab0492d38f323885b0ad80'),
	'resnet101': ('https://paddle-hapi.bj.bcebos.com/models/resnet101.pdparams',
	              '02f35f034ca3858e1e54d4036443c92d'),
}


class BasicBlock(nn.Layer):
	expansion = 1

	def __init__(self,
	             inplanes,
	             planes,
	             stride=1,
	             downsample=None,
	             groups=1,
	             base_width=64,
	             dilation=1,
	             norm_layer=None):
		super(BasicBlock, self).__init__()
		if norm_layer is None:
			norm_layer = nn.BatchNorm2D

		if dilation > 1:
			raise NotImplementedError(
				"Dilation > 1 not supported in BasicBlock")

		self.conv1 = nn.Conv2D(
			inplanes, planes, 3, padding=1, stride=stride, bias_attr=False)
		self.bn1 = norm_layer(planes)
		self.relu = nn.ReLU()
		self.conv2 = nn.Conv2D(planes, planes, 3, padding=1, bias_attr=False)
		self.bn2 = norm_layer(planes)
		self.downsample = downsample
		self.stride = stride

	def forward(self, x):
		identity = x

		out = self.conv1(x)
		out = self.bn1(out)
		out = self.relu(out)

		out = self.conv2(out)
		out = self.bn2(out)

		if self.downsample is not None:
			identity = self.downsample(x)

		out += identity
		out = self.relu(out)

		return out


class BottleneckBlock(nn.Layer):
	expansion = 4

	def __init__(self,
	             inplanes,
	             planes,
	             stride=1,
	             downsample=None,
	             groups=1,
	             base_width=64,
	             dilation=1,
	             norm_layer=None):
		super(BottleneckBlock, self).__init__()
		if norm_layer is None:
			norm_layer = nn.BatchNorm2D
		width = int(planes * (base_width / 64.)) * groups

		self.conv1 = nn.Conv2D(inplanes, width, 1, bias_attr=False)
		self.bn1 = norm_layer(width)

		self.conv2 = nn.Conv2D(
			width,
			width,
			3,
			padding=dilation,
			stride=stride,
			groups=groups,
			dilation=dilation,
			bias_attr=False)
		self.bn2 = norm_layer(width)

		self.conv3 = nn.Conv2D(
			width, planes * self.expansion, 1, bias_attr=False)
		self.bn3 = norm_layer(planes * self.expansion)
		self.relu = nn.ReLU()
		self.downsample = downsample
		self.stride = stride

	def forward(self, x):
		identity = x

		out = self.conv1(x)
		out = self.bn1(out)
		out = self.relu(out)

		out = self.conv2(out)
		out = self.bn2(out)
		out = self.relu(out)

		out = self.conv3(out)
		out = self.bn3(out)

		if self.downsample is not None:
			identity = self.downsample(x)

		out += identity
		out = self.relu(out)

		return out


class ResNet(nn.Layer):
	"""ResNet model from
	`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_
	Args:
		Block (BasicBlock|BottleneckBlock): block module of model.
		depth (int): layers of resnet, default: 50.
		num_classes (int): output dim of last fc layer. If num_classes <=0, last fc layer
							will not be defined. Default: 1000.
		with_pool (bool): use pool before the last fc layer or not. Default: True.
	Examples:
		.. code-block:: python
			from paddle.vision.models import ResNet
			from paddle.vision.models.resnet import BottleneckBlock, BasicBlock
			resnet50 = ResNet(BottleneckBlock, 50)
			resnet18 = ResNet(BasicBlock, 18)
	"""

	def __init__(self, block, depth, num_classes=1000, with_pool=True):
		super(ResNet, self).__init__()
		layer_cfg = {
			18: [2, 2, 2, 2],
			34: [3, 4, 6, 3],
			50: [3, 4, 6, 3],
			101: [3, 4, 23, 3],
			152: [3, 8, 36, 3]
		}
		layers = layer_cfg[depth]
		self.num_classes = num_classes
		self.with_pool = with_pool
		self._norm_layer = nn.BatchNorm2D

		self.inplanes = 64
		self.dilation = 1

		self.conv1 = nn.Conv2D(
			3,
			self.inplanes,
			kernel_size=7,
			stride=2,
			padding=3,
			bias_attr=False)
		self.bn1 = self._norm_layer(self.inplanes)
		self.relu = nn.ReLU()
		self.maxpool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
		self.layer1 = self._make_layer(block, 64, layers[0])
		self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
		self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
		self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

		for m in self.children():
			if isinstance(m, nn.Conv2D):
				n = m._kernel_size[0] * m._kernel_size[1] * m._out_channels#m.weight.shape[0] * m.weight.shape[1] * m.weight.shape[2]
				v = np.random.normal(loc=0., scale=np.sqrt(2. / n), size=m.weight.shape).astype('float32')
				m.weight.set_value(v)
			elif isinstance(m, nn.BatchNorm2D):
				m.weight.set_value(np.ones(m.weight.shape).astype('float32'))
				m.bias.set_value(np.zeros(m.bias.shape).astype('float32'))

		if with_pool:
			self.avgpool = nn.AdaptiveAvgPool2D((1, 1))

		if num_classes > 0:
			self.fc = nn.Linear(512 * block.expansion, num_classes)

	def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
		norm_layer = self._norm_layer
		downsample = None
		previous_dilation = self.dilation
		if dilate:
			self.dilation *= stride
			stride = 1
		if stride != 1 or self.inplanes != planes * block.expansion:
			downsample = nn.Sequential(
				nn.Conv2D(
					self.inplanes,
					planes * block.expansion,
					1,
					stride=stride,
					bias_attr=False),
				norm_layer(planes * block.expansion), )

		layers = []
		layers.append(
			block(self.inplanes, planes, stride, downsample, 1, 64,
			      previous_dilation, norm_layer))
		self.inplanes = planes * block.expansion
		for _ in range(1, blocks):
			layers.append(block(self.inplanes, planes, norm_layer=norm_layer))

		return nn.Sequential(*layers)

	def forward(self, x):
		x = self.conv1(x)
		x = self.bn1(x)
		x = self.relu(x)
		x = self.maxpool(x)

		f = []
		x = self.layer1(x)
		f.append(x)
		x = self.layer2(x)
		f.append(x)
		x = self.layer3(x)
		f.append(x)
		x = self.layer4(x)
		f.append(x)


		return tuple(f)

		# if self.with_pool:
		# 	x = self.avgpool(x)
		#
		# if self.num_classes > 0:
		# 	x = paddle.flatten(x, 1)
		# 	x = self.fc(x)
		#
		# return x


def _resnet(arch, Block, depth, pretrained, **kwargs):
	model = ResNet(Block, depth, **kwargs)
	if pretrained:
		assert arch in model_urls, "{} model do not have a pretrained model now, you should set pretrained=False".format(
			arch)
		weight_path = get_weights_path_from_url(model_urls[arch][0],
		                                        model_urls[arch][1])

		param = paddle.load(weight_path)
		model.set_dict(param)

	return model


def resnet18(pretrained=False, **kwargs):
	"""ResNet 18-layer model

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	Examples:
		.. code-block:: python
			from paddle.vision.models import resnet18
			# build model
			model = resnet18()
			# build model and load imagenet pretrained weight
			# model = resnet18(pretrained=True)
	"""
	return _resnet('resnet18', BasicBlock, 18, pretrained, **kwargs)


def resnet34(pretrained=False, **kwargs):
	"""ResNet 34-layer model

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet

	Examples:
		.. code-block:: python
			from paddle.vision.models import resnet34
			# build model
			model = resnet34()
			# build model and load imagenet pretrained weight
			# model = resnet34(pretrained=True)
	"""
	return _resnet('resnet34', BasicBlock, 34, pretrained, **kwargs)


def resnet50(pretrained=False, **kwargs):
	"""ResNet 50-layer model

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	Examples:
		.. code-block:: python
			from paddle.vision.models import resnet50
			# build model
			model = resnet50()
			# build model and load imagenet pretrained weight
			# model = resnet50(pretrained=True)
	"""
	return _resnet('resnet50', BottleneckBlock, 50, pretrained, **kwargs)


def resnet101(pretrained=False, **kwargs):
	"""ResNet 101-layer model

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	Examples:
		.. code-block:: python
			from paddle.vision.models import resnet101
			# build model
			model = resnet101()
			# build model and load imagenet pretrained weight
			# model = resnet101(pretrained=True)
	"""
	return _resnet('resnet101', BottleneckBlock, 101, pretrained, **kwargs)


def resnet152(pretrained=False, **kwargs):
	"""ResNet 152-layer model

	Args:
		pretrained (bool): If True, returns a model pre-trained on ImageNet
	Examples:
		.. code-block:: python
			from paddle.vision.models import resnet152
			# build model
			model = resnet152()
			# build model and load imagenet pretrained weight
			# model = resnet152(pretrained=True)
	"""
	return _resnet('resnet152', BottleneckBlock, 152, pretrained, **kwargs)